CFTResNet:一个新的跨领域诊断框架,以心血管疾病的可解释性为指导
IEEE journal of biomedical and health informatics
|October 13, 2025
概括
这项研究介绍了CFTResNet,这是一种AI框架,可以通过心脏声音改善心血管疾病的诊断. 它通过选择性微调深度学习模型来提高准确性和可解释性,克服领域转移的挑战.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是一个主要的全球健康问题,导致显著的死亡率.
- 深度学习 (DL) 显示出对自动心血管疾病诊断的承诺,但域移动阻碍了其可靠性.
- 数据采集设备和环境的变化为CVD诊断中的DL模型概括带来了挑战.
研究的目的:
- 提出一个可解释的跨领域诊断框架,CFTResNet,以解决心血管疾病诊断领域的转变.
- 提高心血管疾病检测深度学习模型的适应性和解释性.
- 改进功能表示能力,以分析心脏声 (HS) 信号.
主要方法:
- 开发了CFTResNet,将时间通道融合模块 (TCFM) 与ResNet架构集成,以从HS信号中提取增强的特征.
- 实施了模块稳定性关键性 (MRC) 策略,以识别和微调预训练模型中的特定弱模块,而不是整个网络.
- 利用一种新的方法,通过专注于对关键的,不那么强大的模型组件进行微调来缓解域转移.
主要成果:
- 与传统方法相比,CFTResNet在两个公共心脏声音数据集上显示出更高的诊断准确性.
- 该框架实现了增强的解释性,允许更好地理解诊断过程.
- 观察到跨领域概括的显著改善,表明不同数据源的稳定性.
结论:
- CFTResNet提供了一个可靠的AI辅助工具,用于临床心血管疾病诊断.
- 拟议的方法有效地减轻了域移位,并提高了自动CVD检测中的模型解释性.
- 整合TCFM和MRC策略推动了深度学习在心脏听力分析中的应用.
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